Security is a concern in a variety of transactions involving private information. As an example, iris recognition is a well-accepted and accurate means of biometric identification used in government and commercial systems around the world that enables secure transactions and an added layer of security beyond keys and/or passwords. Due to the increased security provided by iris recognition systems, an increase in use of such systems has occurred around the world.
Biometric identification systems generally compare a recently acquired sample (e.g., a probe) against a gallery of accepted reference samples called enrollments or enrollment images to determine the identity of the probe. A typical enrollment process involves collecting a single sample or selecting a single sample from a plurality of samples acquired during an enrollment session with the subject. When both the enrollment and probe samples are acquired under similar conditions, the samples are expected to exhibit high similarities resulting in confident matches. When the conditions of the collection environment for probes are substantially different than the conditions for enrollment, certain changes may cause the two samples to appear less similar resulting in less confident matches, if a match can be declared at all. As one example, changes in the environment conditions can affect biological characteristics in the collected samples. As another example, changes in the environment conditions can affect non-biological characteristics in the collected samples, such as shadows on the face of a subject caused by surrounding objects or features of the face itself (e.g., shadows from eyelashes due to the position of the sun). This is especially true for mobile applications where users expect anytime and anywhere functionality.
Iris identification or authentication systems generally rely on a single enrollment image as the basis of comparison. For example, such authentication systems generally include an enrollment image for each subject and authenticate the subject by determining which of the multiple enrollment images matches the probe sample. Some authentication systems can include a single enrollment image for each eye of the subject, with authentication occurring by matching one or both eyes of the subject to the enrollment images. The enrollment image of each eye is taken in specific environmental (e.g., lighting) conditions. When users are authenticated in environmental conditions different from the enrollment conditions, their iris may be at a different dilation.
Some traditional biometric processing systems attempt to address biological changes between independent sample collections. For example, some iris processing techniques attempt to compensate for differences such as pupil dilation variation that results when different lighting conditions exist between the enrollment and probe sample acquisitions. (See, e.g., Daugman, J., How Iris Recognition Works, IEEE Transactions on Circuits and Systems for Video Technology, 14, p. 21-30 (2002)). This is generally accomplished by normalizing the texture before matching the subject. The normalization technique assumes that the texture moves linearly as the pupil dilates or constricts. While this method works well over a range of dilations, the scores still suffer as the difference in enrollment and probe sample dilations increases due to more complex movements of the iris. The result is an increase in falsely rejecting authentic subjects.
Some systems attempt to control the lighting conditions that limit their ability to work in any environment. Such systems may work in building access control, but mobile applications generally involve environments not easily controlled. Limiting the installation of systems to instances where the environment can be adequately controlled is not practical for mass deployment. An alternate approach is to build the system to provide a temporary control over the environment. Systems similar to the Secure Electronic Enrollment Kit (SEEK) system attempt to control the light levels influencing pupil dilation by having users insert their face into a shaded cover to block ambient light. This is only a partial solution however, since a subject's body may take a period of time to adjust or acclimate to the temporary environment. For example, a subject who has been in direct sunlight may need to wait seconds before their pupils dilate to a normal enrollment size. Failure to wait long enough can result in failure to be identified by the system. In addition, waiting with one's head in a shaded box for a period of time is inconvenient to the user and limits the maximum number of subjects that can be identified in the equivalent period of time.
Another approach has been to enroll a subject multiple times in each possible environment. This can be problematic because it is often impractical to create all of the possible conditions in a single, convenient setting for an enrolling subject. For example, there is significant complexity in creating a pitch black room, a bright sunny day, and a normal office environment using only a mobile phone. Adding additional enrollments can also create system performance problems. The storage requirements for the enrollment gallery increases by a factor equal to the number of samples permitted per subject. Increasing the size of the enrollment gallery increases the number of statistical trials attempted against every probe that is submitted, which increases the likelihood of falsely accepting an imposter. Increasing the gallery size also increases the time it takes the system to perform an exhaustive set of comparisons, resulting in reduced system responsiveness.
Even if all of the possible environmental combinations are created, the effect of aging is challenging to predict. Everything ages over sufficient time and will start to appear different from an historical enrollment. Over time, certain characteristics of a subject undergo short-term and long-term changes. Pupil dilation is only one example of a short-term adaptation in response to a changing environment that can impact a biometric identification system. Fingerprints swell and contract in response to moisture and water retention. Faces change rapidly with subjects squinting in sunlight, laughing, turning their head, and the like. Over longer periods of time, biometrics suffer from aging, such as wrinkles, sagging, changes in pose, changes in expression, and the like. Due to the aging process, subjects continue to adapt away from a single historical enrollment. As differences become more pronounced, an increased difficulty of being identified by a biometric identification system may occur. If the differences are great enough, a subject must reenroll before being able to continue using the system.
Thus, a need exists for an improved method of enrolling and identifying subjects across a wide range of environmental conditions and over extended periods of time in an efficient and cost-effective manner. These and other needs are addressed by the biometric enrollment systems and methods of the present disclosure.